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This paper deals with an unsupervised approach for land change detection and extraction using bitemporal and multispectral remotely sensed images. It is a statistical approach based on multivariate alteration detection (MAD) transformation combined with a new ChiMerge thresholding method. As opposed to most other multivariate change detection schemes the MAD technique is invariant to affine transformations...
In this paper, we present fusion and classification process of change indices using multitemporal satellites images in the aim to detect the change of surface states after a flood. This process is performed in the framework of Dempster Shafer Theory (DST), which takes into account the imprecision and the ignorance related to data. We apply this process to a study site located at south west of England,...
The aim of this paper is to present a new unsupervised classification method for satellite multispectral images based on affinity propagation (AP) algorithm. Recently proposed, affinity propagation becomes the most widely methods for data clustering. This technique is essentially based on passing of messages between pixels to be automatically classified without any a priori knowledge about the number...
Support Vector Machines (SVM) and Maximum Likelihood (MLLH) are the most popular remote sensing image classification approaches. In the past, SVM and MLLH have been tested and evaluated only as pixel-based image classifiers. Moving from pixel-based analysis to object-based analysis, a fuzzy classification concept is used through eCognition software [1]. In this paper, SVM and MLLH are separately adopted...
The development of robust object-oriented classification approaches suitable for medium to high spatial resolution satellite imagery provides a valid alternative to traditional pixel-based classification approaches. In the past, Support Vector Machines (SVM) have been tested and evaluated only as pixel-based image classifiers. Moving from pixel-based analysis to object-based analysis, a fuzzy classification...
In general, this paper deals with Image Processing using Metaheuristics Optimization Algorithms (IP-MOA). We are focused on supervised classification of remotely sensed images using a clonal selection theory of an Artificial Immune System (AIS). We shall propose a comparative study between the maximum likelihood (MLLH) classifier which is statistical and probabilistic approach and artificial immune...
In this paper we present a possibilistic classifier of multispectral remotely sensed images. This classifier developed in the framework of possibility theory is based on a fusion process using several kinds of combination operators (conjunctive and disjunctive). Unlike the probabilistic classifier which can model only the data uncertainty through a probability measure, the possibilistic classifier...
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